ED journal 20180410（急診期刊閱讀報告）

1. Crit Care Med. 2018 Jan 30
Presented by PGY盧敬文
Supervisor: 蔡銘仁醫師
2018.04.10

2. Background
• The volume of ICU admissions from the emergency department (ED)
has increased by almost 50% between 2001 and 2009
• low availability leads to difficult ICU triage decisions often resulting in
the denial of patients who would otherwise be accepted to the ICU
• deny patients’ admission to the ICU has been shown to be associated
with increased hospital mortality

3. • The rise in ICU admissions has resulted in a 32% increase in ED length
of stay (LOS) for critically ill patients
• in higher volume and/or metropolitan area EDs, up to 87% of all patients
having delay of > 2 hours
• critically ill patients experiencing boarding times of > 6 hours have a
higher risk of inpatient mortality (but conflicting results)
• longer wait times for admission
higher cost, longer LOS, lower adherence to best practices

4. Puzzle ?
• For critically ill ED patients
1. the effect of ED crowding and ICU occupancy on ICU admission
decisions ?
2. the potential association of prolonged delays in admission on in-
hospital morbidity and mortality ?

5. Method:
Study setting and population
• single institution study
• an academic, urban, tertiary care center with a 14-bed closed medical
ICU(MICU)
• Other ICUs can serve as overflow units for patient admissions to the
MICU when there is no bed availability
• The ED contains a five bed area designated for high-acuity patients

6. • The patient cohort:
all adult ED patients (≧18 yr old) for whom MICU admission was
requested from October 1, 2013, to June 30, 2015
• final decision of ICU admission by ICU attending physician
• Post decision made  board in the ED

7. Method:
Study design and measurements
• retrospective cohort study
• objectives:
1) identify predictors of ICU admission decisions (accept vs deny),
• examining the effect of ED and ICU volume on these decisions
2) measure the effect of postconsult ED boarding time on patient
outcome of in-hospital mortality or morbidity
• captured by the presence of persistent organ dysfunction and/or death at 28
days

8. Method:
Statistical methods
Predictors of ICU Admission Decision.
• Multivariable logistic regression was used to determine the odds of
receiving an ICU accept admission decision by patient- and hospital-
related characteristics

9. Predictors of Persistent Organ Dysfunction and/or Death.
• propensity score methods
• All predictors from the triage decision model were included as
candidate
• variables with low common support and high bias were dropped
• Individuals were stratified into quintiles who were similar with
respect to their baseline characteristics

10. RESULTS:
Baseline Characteristics
• A total of 854 consults for ICU admission
• representing 43.7% of all the ICU consults received
• 455 patients (53.3%) were accepted for ICU admission
• 57 patients (12.5%) requiring overflow admission to another ICU

11. Characteristics Accept N = 455 Deny N = 399
Patient-Related
Age, mean (SD)* 60.7 16.7 65.0 17.5
MPM0-III score, median (IQR) 0.15 (0.07, 0.30) 0.13 (0.06, 0.25)
Revised Charlson Score, Comorbidity Index, mean (SD) 3 (1, 5) 3 (1, 5)
Gender (N %)
Female 230 50.5 212 53.1
Male 225 49.5 187 46.9
Race/Ethnicity - (N %)
Caucasian, Non-Hispanic 117 25.7 103 25.8
African American, Non-Hispanic 131 28.8 124 31.1
Hispanic/Latino 146 32.1 114 28.6
Asian/Native American/Other, Non-Hispanic 51 11.2 42 10.5
Unknown 10 2.2 16 4.0
Insurance
Medicare/Private Payor 292 64.2 253 63.4
Medicaid 130 28.6 99 24.8
Other/Unknown 33 7.3 47 11.8
Nursing Home/Facility Pre-Hospital Origin* -
(N, %)
55 12.5 98 24.8
Code Status
No care limitations (e.g., FULL CODE) at time of consult* 443 97.4 341 85.5
No care limitations at time of ICU admission 371 81.5 n/a n/a
No care limitations at hospital discharge 334 73.4 247 61.9
Critical Care Diagnosis Category (N, %)*
Table A3: Baseline patient- and hospital-related characteristics for study cohort of ED patients for whom
Medical ICU admission consult was requested, between 10/2013 and 6/2015.

12. Pulmonary system 189 41.5 123 30.8
Sepsis/septic shock 64 14.1 26 6.5
Cardiac system 54 11.9 28 7.0
Gastrointestinal disorders 45 9.9 36 9.0
Endocrine (including electrolyte derangements) 22 4.8 12 3.0
Other 54 11.9 25 6.3
None 27 5.9 149 37.3
Nightshift timing of consult - (N, %) 247 54.3 215 53.9
ED LOS pre-ICU consult, median (IQR) (hours)* 3.1 (1.9, 5.7) 3.8 (2.1, 7.5)
Hospital-Related (at time of consult)
ED High Intensity Section at Full Capacity - (N, %) 160 35.2 146 36.6
Active ED Patient Volume(Quartiles)
Q1 (low) 118 25.9 97 24.3
Q2-Q3 (medium) 221 48.6 196 49.1
Q4 (high) 116 25.5 106 26.6
Medical ICU at Full Capacity* - (N, %) 117 25.7 131 32.8
Other ICU Patient Volume, percent capacity (Quartiles)
Q1 (low) 152 33.4 115 28.8
Q2-Q3 (medium) 229 50.3 206 51.6
Q4 (high) 74 16.3 78 19.5
Adult Inpatient Volume, percent occupancy (Quartiles)
Q1 (low) 27 5.9 24 6.0
Q2-Q3 (medium) 257 56.5 212 53.1
Q4 (high) 171 37.6 163 40.9
Hospital Course
ED boarding time post-consult, median (IQR) (hours)* 4.2 (2.8, 6.3) 11.7 (6.2, 20.3)
Death in ED* 9 2.0 28 7.0
Hospital LOS, median (IQR) (days)** 8.0 (4.3, 14.7) 6.2 (2.9, 12.5)
Primary Outcome
Persistent Organ Dysfunction/Death (POD+D) - (N, %) 189 41.5 178 44.6
*baseline differences statistically significant p < 0.05

13. RESULTS:
Predictors of ICU Admission
Decision

16. RESULTS:
Predictors of POD+D
Figure 1.
OR [95% CI], 1.79 [1.09–
2.96]/log10 hour increase

17. TABLE 2.
Predictors of Persistent Organ
Dysfunction and/or Death for
Critically Ill Emergency Department
Patients for Whom Medical ICU
Admission Consult Was Completed,
Adjusted for Propensity Score (ICU
Admission): Results From the
Multivariate Regression Model

18. Discussion
• significant effect of MICU bed availability on ICU admission
decisions for critically ill ED patients
• Outcomes between those admitted to the MICU and those
admitted to another ICU as overflow were not significantly
different (36.8% vs 63.2%; p = 0.442).
 potential opportunity to have improved coordination and collaboration
between ICUs to facilitate overflow to offload the ED

19. • use of propensity score analysis helps to account for the selection
bias associated with decision making around ICU admission
• using a composite outcome of mortality and 28-day morbidity
 better elucidate the effect of boarding on negative patient outcomes
• Our model also documents the effect of severity of illness, diagnosis,
and surrogates for frailty (nursing home origin) on ICU decisions
• we did not see an increased effect of ED boarding on POD+D for more
frail or more severely ill patients, which may be related to inadequate
numbers

20. Limitation
1. observational study design and insufficient EHR documentation
• ED providers decided not to request ICU consult after their determination
that a patient may not benefit from ICU services
• detailed information is rarely found in the EHR for the clinical reasoning
2. inability to test for interactions between many of the patient-
related variables due to small sample sizes
3. did not contain a dynamic measure of clinical severity, nor
detailed accounting of the hospital course
4. patient goals of care are often revisited during the patient’s
hospital course

21. 5. We were limited in our investigation of secondary outcomes
related to resource utilization
 did not have data on cost, transfers, readmissions, or other similar
metrics
6. our measures of ED crowdedness were taken at the time of ICU
consult and did not represent the dynamic changes
7. this study reflects a single institution’s ICU admission decision-
making process and an ED-led model of care for boarding critically
ill patients and may not be as applicable

22. Take-home message
1. Critically ill ED patients have lower odds of being accepted for ICU
admission in times of capacity strain in their target ICU, despite bed
availability in other units
2. For all these patients, longer ED boarding times have an
independent negative effect on inpatient mortality and morbidity

23. Thanks for attention!